Rank Exchanges by Orderbook Depth!

Orderbook Depth

ORDERBOOK DEPTH

04.01.2019

STOP RANKING CRYPTO EXCHANGES BY VOLUME!

CHRISTIAN OTT

AUTHOR

Introduction

Ever heard this sentence by an aspiring crypto exchange doing a crowdfunding campaign? “After our ICO is complete, we aim to be a TOP 10 exchange.” Or this one by small crypto projects: “We definitely are in talks with TOP 10 exchanges and want to get listed on them.” Well, I bet you heard them before, because it seems like everybody wants to be in this mysterious TOP 10. But which kind of TOP 10 are they talking about? TOP 10 by number of users? TOP 10 by security? TOP 10 by user-friendliness? No. Unfortunately, the wide audience of speculators and projects seems to care only about one metric regarding crypto exchanges and that is trading volume. In this article, I will discuss why that is not ideal and show an alternative, that should provide better information about the liquidity of an exchange.

Volume is not the best indicator for liquidity

The challenge that comes with volume is not, that it wouldn’t be a good metric. Concerning prices of cryptocurrencies, I think volume should be considered way more in the evaluation of whether a specific cryptocurrency is oversold or overbought, than it is now (Read my article on the Bitcoin ATVWAP for more information). But while comparing exchanges, it is difficult to look at volume, since most of the cryptocurrency exchanges execute their trades in a centralized database and can therefore just trade the same coins back and forth between two bot accounts. While that is not a bad thing in itself, high volume indicates liquidity on the exchange. Users join an exchange and think, they can sell large amounts of a cryptocurrency there, which is apparently not the case, because there might be only a few bots trading.

Orderbook depth by intervals

What users really want to know about a trading pair on an exchange is not, if it has a lot of volume, but whether it has enough liquidity in the orderbook, so that users can buy and sell a substantial portion of coins without moving the price by 10%-20%. Therefore, comparing exchanges and trading pairs by orderbook depth rather than by trading volume should give a way better indication of the liquidity of an exchange/trading pair. To best evaluate the trading liquidity provided by the orderbook, a nested approach could be chosen. For example, you could calculate how much the price of a particular coin would decrease, if you sell its equivalent of 1k USD, 10k USD and 100k USD per market order on a specific trading pair.

As a case study, I did that for the TOP 10 trading pairs by volume (according to CoinMarketCap) of Ether. I looked at the orderbooks of these trading pairs and calculated, by which percentage the price would decrease if you sell 10 ETH, 50 ETH, 100 ETH, 500 ETH and 1000 ETH per market order. Unfortunately, some of them only offered a small insight to their orderbook in their user interface, by only showing the best asks and bids. Nevertheless, it became obvious that trading volume doesn’t necessarily correlate with orderbook liquidity.

OEX and ZBG for example, which were listed on place 1 and 4 of CMC’s list of highest volume trading pairs for Ether on 01-01-2019, didn’t even provide enough liquidity to sell 50 ETH at the current market price. If you would have sold 50 ETH per market order on ZBG, buy orders 10% below the actual price would have been hit. On OEX, the price would have gone down by 32% with a market sell order of 50 ETH. BitForex and Bibox provided more liquidity for lower sell volumes, so you could sell 50 ETH per market order nearly at market price, but if you would have sold 500 ETH, buy orders 80-99% below the actual market price would have been hit. The best liquidity was provided by Huobi, Bitfinex, Okex and Binance, where you could sell more than 1000 ETH per market order, without even hitting buy orders 1% below the actual market price.

Orderbook depth by 1% decline

However, this nested approach is a bit too complicated to display, so I tried to evaluate orderbook liquidity by measuring, how many units of a specific cryptocurrency can be sold via market order for the actual market price. Let’s say the actual market price in the volatile and illiquid cryptocurrency market is the price a token is currently traded at plus/minus 1%. So we would have to measure, how many units of a coin can be sold per market order, without hitting buy orders 1% below the current market price. Of course, it has to be noted, that these numbers change multiple times per second. However, it should still provide interesting insights about which trading pairs’ orderbooks offer high liquidity and which ones do not.

As a case study, I made a snapshot of the orderbooks of 75 exchanges, that had a 24-hour trading volume of above 500k USD in their most frequently traded ETH/Fiat, ETH/Tether or ETH/BTC trading pair on 02-01-2019. By the time the snapshot was taken, on 19 of these 75 exchanges, you could have sold 1000 ETH per market order and wouldn’t have hit buy orders 1% below the current market price. I ranked these 19 exchanges in the following graphic by the amount of ETH, that could have been sold on the exchange per market order, without hitting buy orders 1% below the actual market price. I also added the reported 24-hour volume on these trading pairs (according to CoinMarketCap) to the graphic, showing that trading volume and orderbook depth is totally uncorrelated:

As we can see, the exchanges that have been around for a long time are also the ones, that are providing the highest liquidity. Bitfinex at number 1, Bitstamp on the second place and HitBTC, Kraken and Coinbase Pro positioned on places 3-5, while most newer exchanges, that claim to have a lot of volume, fail to make that list.

Critical discussion

The amount of ETH listed in the graphic originates from the orderbooks provided by the particular exchanges. Alongside the reported trading volume, the entries in an orderbook can be faked. It would be possible for an exchange to make orders disappear in the second a user hits the buy/sell button and make these orders appear again, after the trade has been executed. However, it would certainly damage the reputation of these exchanges, so I am not sure, how long they could sustain a faked orderbook.

Additionally, it has to be noted, that this list cannot be seen as an overall indicator, on how much buying interest there is for Ether across the market. There are interdependencies between the liquidity on different exchanges. Bittrex and Upbit for example, are sharing the same orderbook on the ETH/BTC pair. Accordingly, the number of available buy orders go down on Upbit, if somebody is selling on Bittrex. Some of the exchanges might also display orders on other exchanges in their own orderbook, because they run trading bots on these other exchanges and will execute orders there immediately, if they are hit on their own exchange.

I also want to add, that the exchanges mentioned in the list are not necessarily the best crypto exchanges, just because they provide liquidity. A lot of controversy has been surrounding Bitfinex for the past couple of years and HitBTC has been in the news recently for not allowing their users to withdraw funds. However, this list and the underlying metric of orderbook depth is thought as an improvement towards ranking exchanges by volume, since trading volume is often understood as liquidity, but in the current state of the cryptocurrency market, reported trading volume and liquidity on an exchange are totally uncorrelated.

Wrap-up

In this article, I discussed in which ways liquidity of cryptocurrency exchanges can be evaluated. I showed, that trading volume is not a good indicator for liquidity, because exchanges can simply create a ton of trading volume without any users. A better measurement for liquidity should be orderbook depth, which I expressed as units of a cryptocurrency, that can be sold per market order without hitting buy orders 1% below the actual market price. A case study with data from 75 exchanges revealed, that only 19 provided enough liquidity to sell 1000 ETH per market order, without the price declining by more than 1%. The case study also showed, that trading volume and orderbook depth were completely uncorrelated.